The decision to revascularize blocked coronaries is commonly performed considering anatomical markers extracted from invasive coronary angiography, such as the percentage reduction in lumen diameter. Subjective assessment of angiographically apparent Coronary Artery Disease (CAD) is inadequate due to high degrees of intra-observer and inter-observer variability. Hence, the significance of coronary stenosis is routinely assessed by computer-assisted quantitative coronary angiography. There is strong evidence that this approach has a limited accuracy in evaluating the hemodynamic significance of lesions. In view of the limitations of the pure anatomical evaluation of CAD, the functional index of Fractional Flow Reserve (FFR) has been introduced as an alternative.
Currently, invasively measured FFR is the “gold standard” to determine lesion-specific ischemia, but it has some limitations. The requirement to introduce a wire into the coronary arteries is a potential source of complications, and adverse effects can also be caused by adenosine medication. Furthermore, the logistical effort and financial expense pose a relevant limitation in clinical practice.
Recently, blood flow computations performed using computational fluid dynamics (CFD) algorithms in conjunction with patient-specific anatomical models extracted from medical images (e.g., CT or angiography based scans of the heart and the coronary arteries) have shown great promise in being able to predict invasive, lesion-specific FFR from patient's medical images taken at resting conditions. The CFD-based models combine geometrical information extracted from medical imaging with background knowledge on the physiology of the system, encoded in a complex mathematical fluid flow model comprising partial differential equations which can be solved only numerically. This approach leads to a large number of algebraic equations, making it computationally very demanding.
The computationally demanding aspect of these CFD models and associated image segmentation process prevents adoption of this technology for real-time applications such as intra-operative guidance of interventions. An alternative approach with high predictive power is based on machine learning (ML) algorithms. In this case, the relationship between input data, such as the anatomy of a vascular tree and quantities of interest (e.g., FFR) is represented by a model built from a database of samples with known characteristics and outcome. Once the model is trained, its application to unseen data provides results almost instantaneously.
Previous approaches, both based on CFD and on ML focus on a single imaging modality, either Coronary Computed Tomography Angiography (CCTA) or X-ray Angiography (XA). In many clinical workflows first non-invasive imagining is followed by invasive imaging in case there is an indication for a functionally significant lesion.